Unification of Algorithms for Quantification and Unfolding
| dc.contributor.author | Bunse,Mirko | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:26Z | |
| dc.date.available | 2022-09-28T17:10:26Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Quantification is the supervised learning task of predicting the prevalences of classes in a data sample. Physics literature knows the same task under a different name: unfolding. However, the literature on quantification and the literature on unfolding are largely disconnected from each other. We bridge this interdisciplinary gap by proposing a common framework that integrates algorithms from both fields in a unified form. Instantiations of our framework differ from each other in terms of the loss functions, the regularizers, and the feature transformations they employ. | en |
| dc.identifier.doi | 10.18420/inf2022_37 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39536 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Quantification | |
| dc.subject | Unfolding | |
| dc.subject | Classification | |
| dc.subject | Experimental physics | |
| dc.subject | Machine learning | |
| dc.title | Unification of Algorithms for Quantification and Unfolding | en |
| gi.citation.endPage | 468 | |
| gi.citation.startPage | 459 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Workshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro) |
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